Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework.

ABSTRACT: Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional ligands bridging Proteins-Of-Interest (POIs) and E3 ligases for ubiquitin-proteasome degradation, promising to target the 'undruggable'. While PROTAC research primarily relies on costly and time-consuming wet-lab experiments, deep learning offers potential to accelerate development and reduce expenses. However, many deep learning methods for PROTAC degradation prediction overlook hierarchical molecular representation and protein structural data, hindering data modeling. Moreover, their black-box nature hampers interpretability, failing to provide intuitive insights into substructure interactions within the PROTAC system. This study introduces PROTAC-STAN, a structure-informed deep ternary attention network (STAN) framework for

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12713099 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature